---
title: "Current Status of the Novel Corona Virus (Covid19) Pandemic Outbreak"
output: 
  flexdashboard::flex_dashboard:
    vetical_layout: fill
    orientation: rows
    social: [ "twitter", "facebook", "menu" ]
    source_code: embed

---

```{r setup, include=FALSE}
pacman::p_load(flexdashboard, tidyverse, highcharter, readxl, httr, htmltools, rvest, kableExtra, DT, leaflet, leafpm)
# download data from the EU
# parse download link
covid_eu_url <- read_html("https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide") %>% html_nodes("a") %>% html_attr("href") %>% .[grepl(".xls", .)] 
# download the file to a temporary location
GET(covid_eu_url, write_disk(tf <- tempfile(fileext = ".xls")))
covid_data_eu <- readxl::read_excel(tf)
# Download data from Josh Hopkins University (https://github.com/CSSEGISandData/COVID-19)
process_csse_csv <- function(csvurl, datacol){
  covid_data_csse <- read_csv(csvurl) %>% 
  pivot_longer(cols = ends_with("/20"), names_to = "Date", values_to = datacol) %>% mutate(Date=as.Date(Date, "%m/%d/%y"))
}
csse_files <- c("Confirmed", "Deaths", "Recovered")
covid_data_csse <- csse_files %>% map(~process_csse_csv(glue::glue("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-{.x}.csv"), .x)) %>% reduce(left_join) #%>%  mutate(Mortality_rate=Deaths/Confirmed)

covid_data_who <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/who_covid_19_situation_reports/who_covid_19_sit_rep_time_series/who_covid_19_sit_rep_time_series.csv")

latest_data <- covid_data_csse %>% group_by(`Province/State`, `Country/Region`) %>% arrange(desc(Date)) %>% 
  slice(1) %>% ungroup()  
```

Row {data-height=120}
-----------------------------------------------------------------------

### Total Confirmed Cases

```{r}
valueBox(scales::comma(sum(latest_data$Confirmed, na.rm=TRUE)), icon = "fa-procedures", color="warning") # ion-fitness-outline fa-biohazard ion-medkit-outline ion-ios-medkit-outline
```

### Total Recovered Cases

```{r}
valueBox(scales::comma(sum(latest_data$Recovered, na.rm=TRUE)), icon = "ion-android-favorite-outline", color="success") # fa-briefcase-medical ion-ios-heart-outline
```

### Total Deaths

```{r}
valueBox(scales::comma(sum(latest_data$Deaths, na.rm=TRUE)), icon = "ion-heart-broken", color="danger") # ion-skull-outline, ion-pulse, 
```


### Mean Mortality Rate

```{r}
mort_rate <- sum(latest_data$Deaths, na.rm=TRUE)/sum(latest_data$Confirmed, na.rm=TRUE)
gauge(scales::number(mort_rate*100, accuracy = .01), min = 0, max = 20, symbol = '%', gaugeSectors(
  success = c(0, 2), warning = c(2, 5), danger = c(5,100)
))

# valueBox(scales::percent(mort_rate), icon = "ion-alert", 
#          color=case_when(mort_rate>=0.03 ~ "danger", 
#                          mort_rate>=0.02 & mort_rate<0.03~ "warning", 
#                          mort_rate<0.02 ~"success")) # fa-briefcase-medical ion-ios-heart-outline ion-ios-information-outline ion-alert
```

Row {.tabset .tabset-fade}
-------------------------------------

### Map (leaflet)

```{r confirmed_map_leaflet}
confirmed_cases_latest <- latest_data %>% filter(Confirmed>0) %>% 
  mutate(name= ifelse(is.na(`Province/State`),`Country/Region`, glue::glue('{`Province/State`} ({`Country/Region`})')),
         log_confirmed=log10(Confirmed),
         popup_text=glue::glue('Province/Country: {name}
Confirmed cases: {kableExtra::text_spec(scales::comma(Confirmed), background  = "gold")}
Confirmed deaths: {kableExtra::text_spec(scales::comma(Deaths), background  = "orangered", color="white")} ({scales::percent(Deaths/Confirmed, accuracy=.1)})
Confirmed recovered: {kableExtra::text_spec(scales::comma(Recovered), background  = "limegreen")}') %>% 
           map(~HTML(.x)))  #%>% select(name,lat=  Lat, lon= Long, z=Confirmed)
# Create the leaflet map
leaflet(confirmed_cases_latest) %>% addProviderTiles(providers$CartoDB.Positron) %>% # Esri.WorldStreetMap providers$CartoDB.Positron
  setView(lng = 72.1193378, lat = 9.5361877, zoom = 3) %>% # 16.7432979,29.195003,3z
  addCircles(radius = ~(sqrt(Confirmed)*2500+50000), fillColor="#ff3030", stroke=FALSE, fillOpacity = 0.7,
                   label = ~popup_text, labelOptions = labelOptions(textOnly = FALSE, textsize = "12px")) %>% 
  # addMarkers(popup = ~Name, label = ~Name, 
  #            icon = emojiIcons) %>% 
  addScaleBar(position = "bottomright",
              options = scaleBarOptions(imperial = FALSE)) %>%
  addEasyButton(easyButton(
    icon="fa-globe", title="Zoom out",
    onClick=JS("function(btn, map){ map.setZoom(3); }"))) %>%
  addEasyButton(easyButton(
    icon="fa-crosshairs", title="Locate Me",
    onClick=JS("function(btn, map){ map.locate({setView: true, maxZoom: 6}); }"))) # flyTo

# hcmap("custom/world-palestine-highres", showInLegend = FALSE, fillcolour="#ff3030") %>% 
#   hc_add_series(data = confirmed_cases_latest, fillcolour="#ff3030",
#                 type = "mapbubble", name = "Confirmed Cases", maxSize = '15%') %>% 
#   hc_mapNavigation(enabled = TRUE) %>% 
#   hc_title(text = "Number of confirmed Covid19 cases by Province/State",
#            margin = 40, align = "left",
#            style = list(color = "#2b908f", useHTML = TRUE)) %>% 
#   hc_subtitle(text = glue::glue("Data last updated on {format(max(latest_data$Date), '%d/%m/%Y')}"),
#               align = "left",
#               style = list(color = "#2b908f", fontWeight = "bold")) %>% 
#   hc_exporting(enabled = TRUE) # enable exporting option
```

> Number of confirmed Covid19 cases by Province/Country (last updated on `r format(max(latest_data$Date), '%d/%m/%Y')`)




















### Current Status Summary Table



```{r latest_report_table}
tab_caption <- glue::glue("Current status of the Covid19 pandemic (data updated on {format(max(latest_data$Date), '%d/%m/%Y')})")
latest_data %>% group_by(`Country/Region`) %>% summarise_at(c("Confirmed", "Deaths", "Recovered"), ~sum(.)) %>%  
  mutate(Mortality_rate=Deaths/Confirmed) %>% 
  arrange(desc(Confirmed)) %>% 
  # select(`Country/Region`, Confirmed, Deaths, Recovered, Mortality_rate) %>% 
  # mutate_at(c("Confirmed", "Deaths", "Recovered"), scales::comma) %>% 
  # mutate(Mortality_rate=scales::percent(Mortality_rate, accuracy = .01)) %>% 
  DT::datatable(., colnames = c("Mortality Rate"="Mortality_rate"), rownames = FALSE, 
                style = 'bootstrap', class = 'table-bordered table-condensed', 
                caption = tab_caption,# 
                options = list(pageLength = 15,
                columnDefs = list(list(className = 'dt-center', targets = 1:4)))
                ) %>% formatStyle('Mortality Rate',
  backgroundColor = styleInterval(c(0.01,0.04,0.07), c('limegreen',NA, 'gold', 'orangered'))) %>%  # 'orange', 'orangered', 'firebrick'
  formatPercentage("Mortality Rate", digits = 2) %>% formatRound(c("Confirmed", "Deaths", "Recovered"), digits = 0)
    

```


### Data Sources

The information presented here is sourced from data collected from a range of sources by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and hosted on their [GitHub repository](https://github.com/CSSEGISandData/COVID-19).

This site was compiled on `r format(Sys.time(), '%c')` from data last updated on `r format(max(latest_data$Date), '%d/%m/%Y')`.